Approximately 93 users accounted for 108 distinct Social Mirror sessions between June 22, 2017 and August 3, 2017I.e., 7.3% of people who were sent Direct Messages and invited to participate did so in the time horizon highlighted above. For the remaining analyses, we consider only data collected during each user's first session. 32, 26, and 35 of the 93 sessions were randomly assigned to experimental treatments Viz, Viz+Ideo, and Ideo+Rec, respectively. The median length of each session was 5.8 minutes. Approximately 53 of the 93 unique users completed both the pre and post-experiment survey, and 52 of these 53 answered at least one demographics post-survey question. Table REF  shows several user-level properties, including: pre-treatment survey responses, Twitter personas (average URL alignment, connection diversity, verified status, number of followers/followees), and self-reported demographics (gender, age, and political ideology). Respondents appear to heavily skew male, liberal, and between the ages of 25-44 — likely influenced by the fact that large portions of Americans are under-represented on Twitter [1]}.
To test for the robustness of our randomization, we use a logistic regression to check whether there are any significant differences in the distribution of these properties across treatments. We find no properties have statistically significant coefficients in this model. We assess the model's overall significance by comparing its log-likelihood to a distribution of log-likelihoods generated by 100k logistic regressions, each of which regresses a random shuffling of participant-treatment assignments against the aforementioned properties of interest [2]}. We find the log-likelihood of our actual model is not statistically significant when compared to this distribution (\(p\)  = 0.48), suggesting sound randomization and no significant difference in the measured pre-treatment characteristics of the participants.
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